Course:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Mike X. Cohen

Course:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 22:41

Speaker: : Mike X. Cohen

Course:

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

Difficulty level: Intermediate

Duration: 17:19

Speaker: : Mike X. Cohen

This lecture presents the Medical Informatics Platform's data federation in epilepsy.

Difficulty level: Intermediate

Duration: 27:09

Speaker: : Philippe Ryvlin

Course:

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate

Duration: 1:09:33

Speaker: : Sean Hill

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

Course:

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.

Difficulty level: Intermediate

Duration: 1:47:22

Speaker: : Erin Dickie and John Griffiths

Course:

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.

Difficulty level: Intermediate

Duration: 1:16:10

Speaker: : John Griffiths

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.

This lesson corresponds to slides 1-64 in the PDF below.

Difficulty level: Intermediate

Duration: 1:28:14

Speaker: : Andreea Diaconescu

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

Course:

Introduction to stability analysis of neural models

Difficulty level: Intermediate

Duration: 1:26:06

Speaker: : Bard Ermentrout

Course:

Introduction to stability analysis of neural models

Difficulty level: Intermediate

Duration: 1:25:38

Speaker: : Bard Ermentrout

Course:

Oscillations and bursting

Difficulty level: Intermediate

Duration: 1:24:30

Speaker: : Bard Ermentrout

Course:

Oscillations and bursting

Difficulty level: Intermediate

Duration: 1:31:57

Speaker: : Bard Ermentrout

Course:

Weakly coupled oscillators

Difficulty level: Intermediate

Duration: 1:26:02

Speaker: : Bard Ermentrout

Course:

Continuation of coupled oscillators

Difficulty level: Intermediate

Duration: 1:24:44

Speaker: : Bard Ermentrout

Course:

Firing rate models.

Difficulty level: Intermediate

Duration: 1:26:42

Speaker: : Bard Ermentrout

Course:

Pattern generation in visual system hallucinations.

Difficulty level: Intermediate

Duration: 1:20:42

Speaker: : Bard Ermentrout

- Clinical neuroinformatics (13)
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- EBRAINS RI (6)
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- Standards and best practices (15)
- Tools (12)
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